Ard a target was presented in [76]. In this tactic, investigated making use of simulation

Ard a target was presented in [76]. In this tactic, investigated making use of simulation research, the three=dimensional terrain was modeled as a neuron topological map plus a Dragonfly Algorithm (DA) optimized the movements from the robots. Although this algorithm was not created particularly for agriculture, the scenario can have applications in agricultural robot teams consisting of UAVs and UGVs. Other examples of UAV/UGV coordination approaches is usually discovered in [779]. As talked about earlier, the RHEA project dealt with coordinating aerial and ground robots in precision agriculture [80,81]. In [81], two subtasks of weed and pest control missions were thought of: (a) inspection missions carried out by the aerial group and (b) treatment missions carried out by the ground robots. A Mission Manager was employed to handle the collected information from the several units and centrally compute the trajectories and actions of the robots. Additionally, the ground robot plans have been optimized according to factors such as charges and time. In [82,83], a UGV and UAV independently generated point clouds that represented a map of a field L-Cysteic acid (monohydrate) In Vitro utilizing own onboard cameras. The proposed methodology aimed at proficiently merging the two Diethyl succinate In Vivo individual maps, hence generating a far more precise map which incorporated the surface model as well because the vegetation index. For that reason, collaboration was implicit and arose in the aggregate result of your individual measurements. In [84,85], dual agricultural robot teams consisting of an aerial unit and also a ground unit were proposed, but no specifics around the implementation with the proposed cooperation method were offered. Similarly, the hardware style of a dual UAV/UGV robot systemAgronomy 2021, 11, 1818 Agronomy 2021, 11, x FOR PEER REVIEW12 of 23 12 ofRef. [74] [75] [80,81] [82,83] [84] [85] [86] [87]was proposed in [86]. The objective in the method was to collect images of a crop and then In [82,83], a UGV and UAV independently generated point clouds that represented approach them applying many vegetation indices to be able to decide the crop status. a map of a field making use of own onboard cameras. The proposed methodology aimed at effec A different approach for robot group manage was followed in an additional simulation study [87], tively merging the two individual maps, hence producing a much more correct map which in exactly where the agricultural robot team consisted of three unmanned aerial vehicles and one particular cluded the surface model as nicely as the vegetation index. Hence, collaboration was unmanned ground robot. Every single robot was modeled as a finite state automaton and also the entire implicit and arose from the aggregate result from the person measurements. multirobot system as a discrete event method. It featured a supervisory controller that enIn [84,85], dual agricultural robot teams consisting of an aerial unit plus a ground unit abled heterogeneous agricultural robots to carry out field operations, avoid obstacles, adhere to have been proposed, but no information around the implementation of your proposed cooperation strat a defined formation, and follow a offered path. Table four summarizes the fundamental qualities egy were given. Similarly, the hardware design and style of a dual UAV/UGV robot technique was in the reviewed research. Figure four shows examples of UAV/UGV robot teams. proposed in [86]. The objective of the system was to gather pictures of a crop and then approach them working with several vegetation indices so as to figure out the crop status. Table 4. Summary in the reviewed U.